MétaCan
Menu
Back to cohort
Record W2507746587 · doi:10.1111/ecin.12741

MONETARY AND FISCAL POLICY DESIGN AT THE ZERO LOWER BOUND: EVIDENCE FROM THE LAB

2018· article· en· W2507746587 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEconomic Inquiry · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBank of Canada
FundersFP7 Socio-Economic Sciences and HumanitiesMinistero dell’Istruzione, dell’Università e della Ricerca
KeywordsLiquidity trapEconomicsMarket liquidityZero lower boundFiscal policyMonetary policyMonetary economicsInflation (cosmology)Inflation targetingMacroeconomicsLiquidity riskPhysics

Abstract

fetched live from OpenAlex

The global economic crisis of 2007–2008 has pushed many advanced economies into a liquidity trap. We design a laboratory experiment on the effectiveness of policy measures to avoid expectation‐driven liquidity traps. Monetary policy alone is not sufficient to avoid liquidity traps, even if it preventively cuts the interest rate when inflation falls below a threshold. However, monetary policy augmented with a fiscal switching rule succeeds in escaping liquidity trap episodes. We measure the effect of fiscal policy on expectations, and report larger‐than‐unity fiscal multipliers at the zero lower bound. Experimental results in different treatments are well explained by adaptive learning. ( JEL E70, C92, D83, D84, E52, E62)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.010

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.136
GPT teacher head0.275
Teacher spread0.139 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it